1. Field of the Invention
This invention relates generally to the field of industrial processing, and, more particularly, to a method and apparatus for providing excitation for a process controller.
2. Description of the Related Art
There is a constant drive within industries, such as the semiconductor industry, to increase the quality, reliability and throughput of manufactured workpieces (e.g., integrated circuit devices, microprocessors, memory devices, and the like). In the semiconductor industry, this drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a lot of wafers using a variety of process tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal process tools, implantation tools, etc. The technologies underlying semiconductor process tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the process tools that are currently commercially available suffer certain deficiencies. In particular, such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, precision, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various process tools. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data provided by metrology tools, integrated metrology, or in-situ sensors is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability.
Typically, a control model is used to generate control actions for changing the operating recipe settings for a process tool being controlled based on feedback or feedforward metrology data related to the processing by the process tool. A control model typically includes one or more configurable controller tuning parameters. Exemplary tuning parameters include gain factors that are applied to feedback or feedforward metrology data or weight factors that are applied to current and historical metrology data for data smoothing techniques. Values for the tuning parameters may be calculated or determined empirically.
Certain types of control models estimate various states of the processes being controlled and employ the state estimates for generating control actions. The efficacy of the process controller depends in great part on the effectiveness at which the states can be estimated and updated. Adaptive control techniques are one such class of control schemes where the controller automatically adjusts its model parameters and tuning to account for observed changes in the process itself. These techniques often rely on online model parameter estimation, and the controller settings are continually adjusted to match the current system model derived from the measurements. Adaptive control is useful when the true model of the system is unknown or complicated, since the control law can be based on a simpler model with adjustable parameters. These controllers can be obtained from a variety of design methods including pole placement techniques, minimization of quadratic cost functions, or solving of a Lyapanov function. Adaptive control techniques can enable advanced control concepts such as optimal control, robust control, adaptive control, or robust adaptive control to be used in cases where the system under study is totally unknown, poorly understood, or complicated.
FIG. 1 is a graph showing parameters X and Y for a process under control. For example parameter X may represent an input parameter for the process and parameter Y may represent an output parameter for the process. A process or parameter design window 2 is defined for the process. For example, a design of experiments process may be used to map extremes of the process by running the process at various input settings and obtaining output parameters (e.g., yield or electrical parameters). Typically once the results (e.g., yield or electrical parameters) are analyzed, the process is constrained to a process window 4 to avoid the edges of the design window 2. Due to the constraint and subsequent process control, metrology data 6 collected for the process under control typically lies in the process window 4.
To effectively estimate model parameters in an online fashion, some process characteristics are required. Identification of a process under automatic control is complicated because the actions of the controller mask the underlying behavior of the process. In general, the inputs to the process have to vary in such a way that the model parameters can be uniquely identified. This requirement is called persistent excitation. A difficulty arises because the satisfaction of a typical control objective lowers the amount of excitation as the process reaches a steady state at the desired operating point. At steady state, there is less variation observed in the input and output parameters (i.e., the metrology data 6 is within the process window 4). This situation may reduce the efficacy of the process controller by reducing its ability to estimate various state parameters used in its control model. This situation naturally gives rise to a tighter operating window.
One technique used to provide persistent excitation is to inject small perturbations into the manipulated variables so that the dynamics become visible at the cost of small fluctuations around the process targets. One limitation of this technique is that it is not straightforward to apply standard persistent excitation techniques to many of the process systems and models that are prevalent in a batch processing environment. In many cases, the state variable to be estimated is not directly affected by the commonly chosen input variables. A common example is a timed process, where the state to be estimated is a rate and the processing time is manipulated. Due to reactor fouling or consumable degradation, the chosen processing time can indeed have an effect on the processing rate for future runs. However, the time would have to be adjusted well outside the standard operating range for the rate differences to become noticeable.
A further limitation of a persistent excitation technique is that it injects noise into the processing environment to ensure adequate excitation of the observed parameters. This noise tends to reduce the quality of the workpieces manufactured because features are intentionally formed having characteristics that vary from the established target values. For many processes, a high importance is placed on every batch reaching its targets. In such cases, the noise introduced through the persistent excitation process is undesirable.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.